Elearning Course Curriculum

E-Learning Certificate Programs in Big Data Certificate Program in Accelerated Excellence (E-learning Mode)

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Engineering Big Data with R and Hadoop Ecosystem Ecosystem

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Essential of Applied Predictive Analytics

CSE 7304co

Engineering Big Data with R and Hadoop Ecosystem

Companies collect and store large amounts of data during daily transactions. This data is a combination of structured, semi-structured and unstructured data. The volume of the data being collected daily in many organizations has grown from MB (106) to TB (1012) in the past few years and is continuing to grow at an exponential pace. The very large size, lack of structure and the pace at which it is growing characterize the "Big Data" revolution. To analyze long-term trends and patterns in the data and provide actionable intelligence to managers, this data needs to be consolidated and processed in specialized processes; those techniques form the core of this module. The use cases for the program are "analyzing a customer in near real-time" as applied in Retail, Banking, Airlines, Telecom or Gaming industries. At the end of the program, the participants will be able to set up a Hadoop cluster and write a Map Reduce program that uses pre-built libraries to solve typical CRM data mining tasks like recommendation engines. This course thoroughly trains candidates on the following techniques: 1. SQL querying (with a focus on statistical analysis) 2. Hadoop and Map Reduce methods of programming 3. Designing columnar databases From a tools perspective, this course introduces you to Hadoop. You will learn one of the most powerful combinations of Big Data, viz., "R and Hadoop". In addition, all the essential content required to build powerful Big Data processing applications and to acquire respected industry certifications like Cloudera's Apache Hadoop Developer certification will be covered in the course. The emphasis is not on abstract theory or on mindless coding. The emphasis is, instead, placed on learning concepts and real-world programming techniques. Schedule A 40-hour (20 Sessions), 7-week program each session lasts 2 hours and we meet every alternate day (3 sessions/week)

INTERNATIONAL SCHOOL OF ENGINEERING

http://www.insofe.edu.in

Shakeup Quiz (5 -7min)

Session#

Lecture Session

Lab Session (15-30min)

1

Introduction to Big Data & Applications

Live demo of an Internet-based big data application (10-15min)

2

The Hadoop Eco-system

Different Hadoop installations (20min)

Yes

3

Parallel architectures and concurrent algorithms

Linux shell, Java basics demo (5+20min)

Yes

4

Distributed File Systems, GFS & HDFS

5

HDFS (continued), CDH4 HDFS

Using HDFS from shell & from programs, HDFS Configuration & Log files (30min)

Yes

6

Map Reduce

MR configuration and log files (15min)

Yes

7

Map Reduce (continued)

Word Count with MR (20min)

Yes

8

Map Reduce (continued), YARN, Hadoop Streaming

Hadoop streaming (in some language popular with this batch); CDH4 features demo? (20+ 5-10min)

Yes

9

Sqoop, Hive

Sqoop, Hive demo (5+20min)

Yes

Yes

Demonstration of Word Count in RHadoop, contrast with MR version (30min) More examples on Hive and R-Hadoop. Small demo of H-Base (20-25min)

10

R-Hadoop

11

NoSQL databases including HBase

12

PIG, Oozie

PIG, Oozie demo (20+5min)

13

Machine Learning on Hadoop Mahout

14

Text Search Application on Hadoop

Demonstrate Mahout. Run on movie reco data (30min). MR Demo of Text index building. Assign Text Search homeworks (Homeworks can be done in any one of R-Hadoop / PIG / Hive / Java MR / Hadoop Streaming, as per individual preference) - 25+15min

15

Other ecosystem components

Yes Yes Yes Yes

Yes

Yes Mahout for text classification. Text search student submissions discussion (15+20min). MR demo of SSSP on a non-trivial graph (20min). Assign graph processing homework. PageRank demo on MR and Hama (10+10min).

16

Text Classification, text clustering

17

Graph processing & Applications including SSSP

18

PageRank, BSP, Hama

19

Pregel, Giraph, Social Network Mining

Graph homework student submissions discussion (20min)

Yes

20

Certification & Wrap up

Interaction session with certified professionals (20min)

Yes



Yes

Yes Yes

CSE 7301co

Essential of Applied Predictive Analytics

If you believe that an ability to analyze, forecast and predict using data will help you grow well in your current job, then this 40-hour instructor-led online course is the easiest way to achieve that. Professionals from a diverse set of verticals and horizontals like Marketing, HR, Engineering, Banking, Pharmaceutical, Healthcare, Retail, Telecom, Manufacturing, Data Warehousing, etc. are finding that decisions cannot be taken intuitively anymore. Data is becoming the biggest source of knowledge, differentiation and progress. This course teaches robust and systematic methods that enable gaining insights from data just as a specialist does. At the end of the program, the participants are able to answer business questions such as “who is likely to buy a new product amongst the existing customers”, “which customers are most likely to default on a loan or an insurance payment” and “of a given set of transactions, which are most likely to be fraudulent”. This course thoroughly trains candidates on the following techniques: Pre-processing Techniques: Graphical Visualization, Handling Missing Values, Data Standardization; Predictive Models: Decision Trees, Linear Regression, Logistic Regression; Model Selection Techniques: Concepts of Overfitting, Bias and Variance; Cross Vali dation; Error metrics like Precision, Accuracy and Recall; Introduction to solving analytics problems using R. Schedule: A 40-hour, 8-week program. Each session lasts 2 hours. Day 1: Introduction to Big Data; Course Motivation; Logistics; Analysis through Data Visualization Day 2: Understanding the “business case” and defining a solution framework Day 3: An introduction to R programming language and environment Day 4: Techniques of Pre-processing data (Binning, Normalizing, Filling missing values, removing noise) Day 5: Data Pre-processing—continued Day 6: Traps and Errors: Confusion matrix, Analyze False positives and False Negatives from a problem perspective; Different error measures used in Forecasting Day 7: Model Selection: K-fold validation Day 8: Introduction to Decision Trees and their structure Day 9: Construction of Decision Trees through simplified examples; Choosing the “best” attribute at each non-leaf node; Entropy; Information Gain



Day 10: Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with numerical variables; other measures of randomness Day 11: Inductive learning from a 500-ft view; Issues in inductive learning like curse of dimensionality; Overfitting; Bias-Variance tradeoff Day 12: Pruning a Decision Tree, Cost as a consideration; Unwrapping Trees as rules Day 13: A mathematical model for association analysis Day 14: Large itemsets and Association Rules; Apriori: Constructs large itemsets with minisup by iterations Day 15: Interestingness of discovered association rules; Application examples; Association analysis vs. Classification Day 16: Using Association Rules to compare stores; Dissociation Rules; Sequential Analysis Using Association Rules Day 17: Data visualization and Story-telling: Anatomy of a graph Day 18: Animated graphs, BI dashboards and the latest trends in data visualization Days 19 and 20: An end-to-end case study in R involving understanding the data, filling the missing values, applying and assessing models and reporting the results.



Mentors Profiles Dr. SREERAMA K MURTHY Co-founder and CEO, Teqnium Consultancy Services PhD in Data Mining, Johns Hopkins University Classes Taught Engineering Big Data with R and Hadoop Ecosystem Brief Profile Ph.D. - Johns Hopkins University M.Tech. – IIT, Chennai (Madras) B.E. - NIT, Allahabad 17 years of work experience after Ph.D. (USA: 5 years, India: 11 years) 21 US Patent applications (8 issued), 2 Indian patent applications 

Many invention disclosures, numerous journal and conference papers.

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Designed, managed, built and deployed large software systems.

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Technocrat,

combining

love

for

technology

with

entrepreneurship

and

business

management. 

Helped conceptualize business plans of three ventures.

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Obtained millions of dollars in funding.

Chairman & CEO - Teqnium Consultancy Services Director, Technology - Globarena ITeknowledge Pvt Ltd Managing Director - Globarena Web Technologies Senior Manager and Head, E-Commerce Research group - IBM India Research Lab Researcher - Siemens Corporate Research Areas of Expertise: Technology Enabled Education and Training, e-Skilling, Outsourced R&D, Data Mining, Digital Security, Healthcare Informatics Specialties: Education Strategy, Role of Technology in Skills Development, Instructional Design, Research, Intellectual Property, Novel Product Design



Dr. DAKSHINAMURTHY V KOLLURU President, International School of Engineering PhD in Materials Science and Engineering, CMU

Classes Taught Essentials of Applied Predictive Analytics

Brief Profile Ph.D. – Carnegie Mellon University (CMU) M.S. – Carnegie Mellon University (CMU) B.E. – NIT, Tiruchirapalli 15 years of work experience after Ph.D. in diverse organizations ranging from Defense Research to Web startup and mid-size IT services companies. President - International School of Engineering, Chief Research Officer - Prithvi Information Solutions Ltd., Hyderabad, Founder and Managing Director - Axaya Cybertech Pvt Ltd, Co-founder and Managing Director - Globarena ITeknowledge Pvt. Ltd Scientist - Defence Metallurgical Research Laboratory, Hyderabad, During his years of experience as a scientist and entrepreneur, Murthy has applied his strengths in logical thinking, math and science to solving industrial and societal problems, designing solutions from fundamentals, identifying, training and motivating high quality individuals, and to articulating the findings in a lucid manner to all the stakeholders. Over the past few years, Dr. Murthy has been actively teaching Data Analytics to working professionals with wide range of experience and from diverse industries. He has also been consulting on Data Science projects with Fortune 25 to IT Services to Startup companies. During his years of experience as a scientist and entrepreneur, Dr. Murthy has applied his strengths in logical thinking, math and science to solving industrial and societal problems, designing solutions from fundamentals, identifying, training and motivating high quality individuals, and to articulating the findings in a lucid manner to all the stakeholders. He built the Business Analytics and Optimization division of a mid-tier IT services company from scratch and filed for 5 patents in Retail and Telecom Analytics, during which time he also acquired Fortune 500 clients and turned the division into a profitable delivery center.



Fee Structure Program Fee for Each Individual Module: 

For International Students: $9 for Application Fees and $640 for Program Fees

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For Indian Students: Rs. 500 for Application Fees and Rs. 35,000 for Program Fees

Program Fee for Two Modules: 

For International Students: $9 for Application Fees and $960 for Program Fees

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For Indian Students: Rs. 500 for Application Fees and Rs. 54,000 for Program Fees

For more details, please visit: http://insofe.edu.in/init/default/elearning_engineering_big_data

For any queries; Contact: - +91 9502334561 or email us at [email protected]



International School of Engineering Address: 1st Floor, Plot No 63/A, Road No 13, Film Nagar, Jubilee Hills, Hyderabad – 500033 Contact Number: +91 9618 483 483; Website: www.insofe.edu.in Facebook: www.facebook.com/insofe Linkedin: http://goo.gl/VzC9s Twitter: @INSOFEedu Slideshare: http://www.slideshare.net/INSOFE



Elearning Course Curriculum

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